The goal of any data warehouse journey is timely, unfettered access to fully integrated, de-siloed, cleansed, and modeled data to best empower and inform the business through reports, analytics capabilities, and visualizations.   To work toward this goal, you need to assemble a complementary suite of data management capabilities, including robust back-end tools to ingest, store, cleanse, and serve data as rapidly as possible, and also provide self-serve front-end tools to enable the business to easily explore, discover, and mine data for relevant insights. That is a lot to assemble and maintain, but by basing architectural decisions on modern data integration approaches and leveraging cloud and on-premises solutions, your hybrid data warehouse will handle any data type and rate of ingestion, and, more importantly, will deliver clean, modeled data to meet analytical needs quickly and with extreme agility.   This session will review the case study of building a hybrid data warehouse and will lay the groundwork for architects who wish to embrace a scalable, flexible architecture for a comprehensive view of their business. Specifically, we will discuss:

• The advantages of a hybrid data warehouse approach
• Essential components of a vendor-agnostic scalable hybrid architecture
• Data virtualization fundamentals
• Adoption considerations